Human language is exceedingly rich and complex. The job of translating human language can be highly challenging as a result. When translating a sentence from one language to another (or, in industry parlance, from a “source” to a “target” language), it is rarely enough to substitute, one by one, words in the target language for words in the source language. In most cases, the job of the translator is to write, anew, a sentence in the target language that are, as near as possible, equivalents for each sentence in the source language.
This goal of achieving equivalence is the central challenge in translation. How difficult it is to do that depends in part on the nature of the material being translated. While there are texts that are relatively easy to translate (a sentence such as “press the start button”, for example, could be translated into most languages fairly readily), most texts are not. A sentence like “ain't life grand?” for example, which might appear simple at first, actually has several levels of meaning and nuance embedded in it. The sentence not only tells us that “life is wonderful”, it also says something about the speaker. It may be difficult to find an expression in another language that has the same literal meaning, is colloquial, is snappy, and similarly calls to mind a speaker who is likely to be a man from a certain era.
These multiple layers of meaning, make finding true translation equivalents difficult. And because of this difficulty, which applies when translating all but the most straightforward of texts, much of the material that could theoretically be translated, is not. The overwhelming majority of the world's printed information, and especially literature, poetry, comedy, musical lyrics, and similar rich forms of text, have not been translated.
Systems have been developed to facilitate the process of carrying out human translation. Such systems fall into two types.
The most common type of conventional system, which constitutes the overwhelming majority of conventional systems, takes a “one-text, one-translator” approach (term coined for the purposes of this filing); that is, these systems rely on a single translator to translate each portion of a source text. Designed for efficiency and low cost, this type of system is problematic from a quality standpoint because of human error and the limits in creativity and judgment inherent to any one person. Generally speaking, a single translator cannot, over time, match the ability of a group of translators to creatively produce target-language equivalents of complex texts.
The second type of conventional system—the “redundant translation” type (term coined for the purposes of this filing)—is designed to have the same text translated by multiple translators. A limitation of these systems is that they do not put the translators in communication with each other. Because of the way these systems control quality, i.e., matched translations, produced independently, are regarded as “correct,” translators are required to work independently. Because there is no collaborative benefit to such systems, they suffer from a similar quality limitation as “one-text, one-translator” systems. In fact, these systems have not been designed to achieve higher quality than “one-text, one-translator” systems; they are intended primarily to reduce cost. Moreover, the incentives that such systems typically employ (for example, some systems find translators by inviting laypersons to take part as an exercise in language learning) are not necessarily applicable to professional and/or experienced translators, and are not necessarily consistent with producing high-quality translations of texts.
A need exists for a system that taps the collective creativity, intelligence and/or judgment of a group of human and/or automated translators, and optionally other human and/or automated participants, to maximize the quality of translation of challenging texts.